submitted by /u/matijash [link] [留言]

When one feature touches multiple repositories, the Git workflow can quickly become repetitive. You may need to: Create the same branch in several repositories Pull the latest changes in each project Check the status of every repository Remember which repositories belong to the same task Move between directories repeatedly A typical workflow might look like this: cd api git switch -c fix/123-auth cd ../frontend git switch -c fix/123-auth cd ../worker git switch -c fix/123-auth I built RepoFleet to simplify this workflow. What Is RepoFleet? RepoFleet is an issue-centered CLI tool for managing Git workflows across multiple repositories. Instead of managing each repository separately, you create one issue context: rf issue create 123 --name auth --type fix RepoFleet creates and manages the related branches across your repositories. You can then check everything from one place: rf issue status One issue. Multiple repositories. One workflow. The Problem Imagine that one feature requires changes in three repositories: api frontend worker Without RepoFleet, you might need to run: cd api git fetch git switch -c feature/123-auth cd ../frontend git fetch git switch -c feature/123-auth cd ../worker git fetch git switch -c feature/123-auth Later, you need to repeat a similar process to check status, pull changes, or push branches. This works, but it becomes inconvenient when you manage many tasks across multiple repositories. Why I Built It At work, our codebase was split into multiple repositories. After the split, one task could require changes in several projects. I repeatedly had to: Create matching branches Switch between project directories Fetch updates Check Git status in every repository Remember which repositories belonged to each issue I wanted a workflow centered around the issue, rather than individual repository directories. That idea became RepoFleet. _Example Workflow _Create an issue context rf issue create 123 --name authentication --type feature Add repositor

X's head of product, Nikita Bier, admitted in a post on Monday that X's algorithm was "missing" data about surfacing posts from people who you've followed back. Now, he says a tweak will "boost visibility of your posts to your mutuals," hopefully enhancing the sense of community instead of highlighting and spreading random arguments, but […]

Marc Lou read a tweet, slept on it, and woke up still annoyed. The tweet, from Pieter Levels, was about all the fake revenue screenshots on X. By the next evening Lou had built a thing to fix it. By the day after that, the thing had made $20,378. That is the part everyone retweets. I want to walk you through it, and then I want to show you the line in his own year-end letter that complicates the whole legend. The setup Lou got fired by Tai Lopez in November 2021, was broke and depressed, and moved to Bali. He started shipping tiny products in public, copying the playbook of, yes, Pieter Levels. His breakout was ShipFast , a Next.js starter kit that did $40,000 in its first month in September 2023. By December 2025 he was running 15 startups generating about $84,900 a month, with cumulative revenue past $2.26 million, per his verified TrustMRR data. The reason I trust his numbers more than most is that he verifies them through Stripe on his own product, TrustMRR , which brings me to the 24-hour story. The moment something worked, absurdly fast TrustMRR exists to kill fake MRR screenshots. You connect a read-only Stripe key, and it shows your verified revenue on a public page nobody can edit. Lou built it in a day on top of his own boilerplate, which is the cheat code here. He was not starting from zero, he was starting from ShipFast. "TrustMRR is 24 hours old and was built in 24 hours." @marc_louvion on X He monetized it with sidebar ad slots. He listed them at $299 a month, then raised the price each time one sold, all the way to $1,499. In his newsletter he wrote that within three days every slot was gone and the side project had made $20,378. He called it the third fastest-growing thing he has ever built. Five days in, he posted the run-rate dream out loud. "20/20 spots filled! TrustMRR went from $0 to $18,380 MRR in 5 days. That's $220,000 ARR if I'm allowed to dream a little" @marc_louvion on X It kept going. By December 2025 TrustMRR was his single biggest inco

New York has become the first state to temporarily halt approval of large data centers, as Gov. Kathy Hochul argues the AI-driven building boom shouldn’t come at the expense of higher electricity costs, water supplies, or local control.

submitted by /u/cachebags [link] [留言]

The Iranian government exploited well-known flaws in cellphone networks to locate and then strike U.S. military personnel in the build-up and beginning of the war.

New York’s data center moratorium may become the blueprint for anti-AI movement.

The only way to go fast is full YOLO mode in your coding agent. I've got the local sandbox figured out (pro tip: Incus VMs work great) but I wanted to keep my agents from doing things like inadvertently blowing up my cloud services or chasing a prompt to POST to some random website. I struggle most with this on my side projects where my permission model isn't quite as robust as it is at the office. I started with a firewall on the Incus container but every time the agent needed access to somethi

“We have no choice but to address the challenges created by these massive facilities,” New York governor Kathy Hochul said. The executive order will pause construction for one year.

Health officials have not confirmed a source yet—and there may be multiple sources.

The RingConn 3 actually looks like real jewelry, not a wearable -- but its fitness tracking and headache detection features are disappointing.

submitted by /u/IanisVasilev [link] [留言]

I wanted to run AI from inside the JVM. I started out with the standard REST sidecar, ripped that out to use Project Panama (Foreign Function & Memory API) in the new JDK versions to interface directly with llama.cpp. I still wasn't happy with how that functioned, so I built libargus.cc to get a clean ABI to expose a structured API up in the JVM landscape. It still uses Project Panama to interface directly with llama.cpp, whisper.cpp, and ggml compute graphs. I have zero-allocation on the hot pa